Analyzing Periodicity and Saliency for Adult Video Detection
This addresses content-based pornography prevention, but it is incremental as it builds on existing multi-modality methods.
The paper tackled adult video detection by analyzing periodicity in audio and saliency in visual frames, then combining them for multi-modal semantics. Experimental results showed the approach outperformed state-of-the-art methods.
Content-based adult video detection plays an important role in preventing pornography. However, existing methods usually rely on single modality and seldom focus on multi-modality semantics representation. Addressing at this problem, we put forward an approach of analyzing periodicity and saliency for adult video detection. At first, periodic patterns and salient regions are respective-ly analyzed in audio-frames and visual-frames. Next, the multi-modal co-occurrence semantics is described by combining audio periodicity with visual saliency. Moreover, the performance of our approach is evaluated step by step. Experimental results show that our approach obviously outper-forms some state-of-the-art methods.